Edge detection is important for a number of image processing and document reproduction applications, such as border removal, skew correction and for size based processing, such as auto-crop, fit-to-page, borderless printing, orientation detection etc.
When a paper document is scanned by a scanner to produce a digital image of the document, the resulting digital image is often skewed. The causes of a skewed image may be that the document was not fed properly, or that the paper transport mechanism is out of alignment. The skew of a document image produces an unpleasant effect on viewing an image. In addition, it results in additional complexities in optical character recognition, document segmentation, and document analysis.
Also, when documents or various sizes and/or thicknesses are scanned by a scanner, unwanted borders or patches (which are typically dark or white in colour) may be present at one or more edges of a document in a resulting digital image. Such borders or patches may be due to a document not matching the scanning area or a document preventing the scanner lid from being closed properly, for example, and are undesirable in a document image due to the extra data storage and/or ink requirements they create.
Effective page edge detection is a typical component of border removal and skew correction in digital images. However, detected edges can also be utilized for size-based processing of a digital document image, such as auto-crop, fit-to-page, borderless printing, and orientation detection applications.
Conventional methods for software-based or in-product page edge detection exhibit limitations in one or more of the following variations: noise of Charge-Coupled Device (CCD) sensor outputs, document content and color, scanner/copier hardware, and the way the input is read (either as a whole image or in swaths).
Also, for light documents against a light background, and dark documents against a dark background, it is challenging to detect page edges, since the statistics of the foreground and background may be similar.